Library

@article{berthon_trajectories_2024,

title = {Trajectories of AI technologies: Insights for managers},

author = {Pierre Berthon and Taylan Yalcin and Ekin Pehlivan and Tamara Rabinovich},

url = {https://www.sciencedirect.com/science/article/pii/S0007681324000284},

doi = {10.1016/j.bushor.2024.03.002},

issn = {0007-6813},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-24},

journal = {Business Horizons},

volume = {67},

number = {5},

pages = {461–470},

series = {SPECIAL ISSUE: WRITTEN BY CHATGPT},

abstract = {Generative artificial intelligence (GenAI) has long been considered a technology for the future. With the release of the chatbot ChatGPT 4, many now feel the future has arrived. Long in gestation, this new technology promises many benefits to humankind, but worries persist that as AI technology scales and comes to rival or exceed human intelligence, the servant may become the master. Amid such hyperbole, the more nuanced trajectories of this technology have been neglected. In this article, we use the Trajectories of Technology (ToT) framework developed by Berthon and colleagues to explore the disparate paths that AI has taken and will take in the coming years, especially in the form of chatbots. This framework provides managers with a conceptual tool to strategically plan for the enormous promises and perils of AI in general and of chatbots specifically.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{ferraro_paradoxes_2024,

title = {The paradoxes of generative AI-enabled customer service: A guide for managers},

author = {Carla Ferraro and Vlad Demsar and Sean Sands and Mariluz Restrepo and Colin Campbell},

url = {https://www.sciencedirect.com/science/article/pii/S0007681324000582},

doi = {10.1016/j.bushor.2024.04.013},

issn = {0007-6813},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-24},

journal = {Business Horizons},

volume = {67},

number = {5},

pages = {549–559},

series = {SPECIAL ISSUE: WRITTEN BY CHATGPT},

abstract = {Generative artificial intelligence (GenAI) presents a disruptive innovation for brands and society, and the power of which is still yet to be realized. In the context of customer service, gen AI affords companies new possibilities to communicate, connect, and engage customers. This article draws on scholarly research and consultation with customer service leaders to present and discuss the possibilities for GenAI in the context of customer service, specifically GenAI chatbots. Importantly, this article presents potential paradoxes of GenAI-enabled customer service, adding to the debate about the role and impact of GenAI for brands. Specifically, we present six paradoxes of GenAI customer service: (1) connected yet isolated, (2) lower cost yet higher price, (3) higher quality yet less empathy, (4) satisfied yet frustrated, (5) personalized yet intrusive, and (6) powerful yet vulnerable. For each paradox, we suggest brand response strategies to mitigate downside and manage potential upside.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{rizzo_managers_2024,

title = {Do managers trust AI? An exploratory research based on social comparison theory},

author = {Cristian Rizzo and Giacomo Bagna and David Tuček},

url = {https://www.emerald.com/insight/content/doi/10.1108/md-10-2023-1971/full/html},

doi = {10.1108/MD-10-2023-1971},

issn = {0025-1747},

year  = {2024},

date = {2024-07-01},

urldate = {2024-11-22},

journal = {Management Decision},

volume = {ahead-of-print},

number = {ahead-of-print},

abstract = {The purpose of this study is to investigate managers’ decision-making processes when evaluating suggestions provided by human collaborators or artificial intelligence (AI) systems. We employed the framework of Social Comparison Theory (SCT) in the business context to examine the influence of varying social comparison orientation levels on managers’ willingness to accept advice in their organization.,A survey was conducted on a sample of 192 US managers, in which we carried out an experiment manipulating the source type (human vs AI) and assessing the potential moderating role of social comparison orientation. Results were analyzed using a moderation model by Hayes (2013).,Despite the growing consideration gained by AI systems, results showed a discernible preference for human-generated advice over those originating from Artificial Intelligence (AI) sources. Moreover, the moderation analysis indicated how low levels of social comparison orientation may lead managers to be more willing to accept advice from AI.,This study contributes to the current understanding of the interplay between social comparison orientation and managerial decision-making. Based on the results of this preliminary study that used a scenario-based experiment, future research could try to expand these findings by examining managerial behavior in a natural context using field experiments, or multiple case studies.,This is among the first studies that examine AI adoption in the organizational context, showing how AI may be used by managers to evade comparison among peers or other experts, thereby illuminating the role of individual factors in affecting managers’ decision-making.},

note = {Publisher: Emerald Publishing Limited},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{cuellar_does_2024,

title = {Does Information About AI Regulation Change Manager Evaluation of Ethical Concerns and Intent to Adopt AI?},

author = {Mariano-Florentino Cuéllar and Benjamin Larsen and Yong Suk Lee and Michael Webb},

url = {https://doi.org/10.1093/jleo/ewac004},

doi = {10.1093/jleo/ewac004},

issn = {8756-6222},

year  = {2024},

date = {2024-03-01},

urldate = {2024-11-22},

journal = {The Journal of Law, Economics, and Organization},

volume = {40},

number = {1},

pages = {34–75},

abstract = {We examine the impacts of potential artificial intelligence (AI) regulations on managers’ perceptions on ethical issues related to AI and their intentions to adopt AI technologies. We conduct a randomized online survey experiment on more than a thousand managers in the United States. We randomly present managers with different proposed AI regulations, and ask about ethical issues related to AI and their intentions related to AI adoption. We find that information about AI regulation increases manager perception of the importance of safety, privacy, bias/discrimination, and transparency issues related to AI. However, there is a tradeoff; regulation information reduces manager intent to adopt AI technologies. Moreover, information about regulation increases manager intent to spend on developing AI strategy including ethical issues at the cost of investing in AI adoption, such as providing AI training to current employees or purchasing AI software packages. (JEL: K24, L21, L51, O33, O38)},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{koponen_work_2023,

title = {Work Characteristics Needed by Middle Managers When Leading AI-Integrated Service Teams},

author = {Jonna Koponen and Saara Julkunen and Anne Laajalahti and Marianna Turunen and Brian Spitzberg},

url = {https://doi.org/10.1177/10946705231220462},

doi = {10.1177/10946705231220462},

issn = {1094-6705},

year  = {2023},

date = {2023-12-01},

urldate = {2024-11-24},

journal = {Journal of Service Research},

pages = {10946705231220462},

abstract = {Artificial intelligence (AI) is a significant part of digital transformation that signifies new requirements for middle managers in AI-integrated work contexts. This is particularly evident in financial service industries. Given the significance and rapidity of this technological transition, this case study investigated how middle managers perceived the impacts of AI system integration on their work characteristics. Interview data were gathered from 25 middle managers of a company providing financial services. The data were analyzed using the Gioia method. The findings showed that the AI systems applied in the case company were perceived as technical tools (mechanical AI) or coworkers (thinking AI and feeling AI), which had different impacts on middle managers’ work characteristics and the relationship between humans and AI systems. The middle managers’ work characteristics included contextual, task, competence, social, and relationship characteristics. Regarding the relationship characteristics, this study shows theoretically distinct human–AI relationship types. The findings are organized into a conceptual framework. AI system integration in service teams is a complex phenomenon that makes middle managers’ work more demanding and requires balancing and managing multiple challenges and dialectical tensions. The findings inform the selection and training of managers according to changing work characteristics in the digital age.},

note = {Publisher: SAGE Publications Inc},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{gaczek_collaboration_2023,

title = {Collaboration with machines in B2B marketing: Overcoming managers' aversion to AI-CRM with explainability},

author = {Piotr Gaczek and Grzegorz Leszczyński and Amira Mouakher},

url = {https://www.sciencedirect.com/science/article/pii/S0019850123001669},

doi = {10.1016/j.indmarman.2023.09.007},

issn = {0019-8501},

year  = {2023},

date = {2023-11-01},

urldate = {2024-11-22},

journal = {Industrial Marketing Management},

volume = {115},

pages = {127–142},

abstract = {This paper links negative emotions to AI and examines their influence on aversion to collaborating with AI in customer relationship management. It aims to understand working with AI-CRM and considers AI-based recommendations in marketing decision-making. This article is empirically supported by three experimental studies involving over seven hundred B2B customer relationship management-committed managers. It demonstrates that eXplainable Artificial Intelligence (XAI) is a tool that can help mitigate the dark sides of collaboration with AI and increase the propensity to incorporate its suggestions in customer relationship management decision-making.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{chen_drivers_2022,

title = {Drivers of salespeople’s AI acceptance: what do managers think?},

author = {Jing Chen and Wenkai Zhou},

url = {https://doi.org/10.1080/08853134.2021.2016058},

doi = {10.1080/08853134.2021.2016058},

issn = {0885-3134},

year  = {2022},

date = {2022-04-01},

urldate = {2024-11-22},

journal = {Journal of Personal Selling & Sales Management},

volume = {42},

number = {2},

pages = {107–120},

abstract = {This research is among the first to examine salespeople’s acceptance of AI (artificial intelligence) and we investigate the drivers of their AI acceptance from the perspective of the managers. In this study, we propose and empirically demonstrate that perceived ease of use, self-efficacy, perceived management support, and digitalization are positively related to salespeople’s acceptance of AI. Moreover, we show that digitalization mediates the relationship between salespeople’s prospecting/adaptive selling capabilities and their AI acceptance. The results suggest that in order to incentivize AI acceptance, managers need to build adequate digital infrastructure, cultivate organizational support to encourage AI adoption and usage, provide professional training to educate salespeople on the proper usage of AI, and reduce salespeople’s perceived risk of AI usage. Theoretical and managerial implications are discussed subsequently.},

note = {Publisher: Routledge 

_eprint: https://doi.org/10.1080/08853134.2021.2016058},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{keding_managerial_2021,

title = {Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions},

author = {Christoph Keding and Philip Meissner},

url = {https://www.sciencedirect.com/science/article/pii/S0040162521004029},

doi = {10.1016/j.techfore.2021.120970},

issn = {0040-1625},

year  = {2021},

date = {2021-10-01},

urldate = {2024-11-24},

journal = {Technological Forecasting and Social Change},

volume = {171},

pages = {120970},

abstract = {AI-augmented decision-making processes promise to transform strategic decisions around innovation management. However, despite a growing body of research on algorithmic management, very little is known about the behavioral effects of the AI-augmented decision-making process. This article utilizes a psychological perspective to research the interaction of artificial intelligence and human judgment, suggesting that AI-based advice affects human decision-making behavior and skews perceptions of decision outcomes. We present a vignette-based decision experiment involving 150 senior executives to examine the perception of AI-augmented decision-making at the individual level. In contrast to earlier research on algorithm aversion, we find that employing AI-based advisory systems positively affects choice behavior and amplifies decision quality perception. We further show how this overreliance on an AI-augmented decision-making process can be explained through both a higher degree of trust in the advisor and the attribution of a more structured process. This paper contributes to the emerging discussion as to the role of AI in management and the novel phenomenon of algorithm appreciation by investigating the interplay of human and artificial intelligence in strategic decision-making to show that AI-based advice is perceived as more trustworthy than human advice in an R&D investment context.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{sowa_cobots_2021,

title = {Cobots in knowledge work: Human – AI collaboration in managerial professions},

author = {Konrad Sowa and Aleksandra Przegalinska and Leon Ciechanowski},

url = {https://www.sciencedirect.com/science/article/pii/S014829632030792X},

doi = {10.1016/j.jbusres.2020.11.038},

issn = {0148-2963},

year  = {2021},

date = {2021-03-01},

urldate = {2024-11-24},

journal = {Journal of Business Research},

volume = {125},

pages = {135–142},

abstract = {Current technological developments, as well as widespread application of artificial intelligence, will doubtlessly continue to impact how people live and work. In this research, we explored synergies between human workers and AI in managerial tasks. We hypothesized that human-AI collaboration will increase productivity. In the paper, several levels of proximity between AI and humans in a work setting are distinguished. The multi-stage study, covering the exploratory phase in which we conducted a study of preferences using 10-item Likert scale, was conducted with a sample of 366 respondents. The study focused on working with different types of AI. The second and third phase of the study, in which we primarily used qualitative methods (scenario-based design combined with semi-structured interviews with six participants), focused on researching modes of collaboration between humans and virtual assistants. The study results generally confirmed our hypothesis about increased productivity due to enhanced human-AI collaboration, proving that the future of AI in knowledge work needs to focus not on full automation but rather on collaborative approaches where humans and AI work closely together.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{black_ai-enabled_2020,

title = {AI-enabled recruiting: What is it and how should a manager use it?},

author = {J. Stewart Black and Patrick Esch},

url = {https://www.sciencedirect.com/science/article/pii/S0007681319301612},

doi = {10.1016/j.bushor.2019.12.001},

issn = {0007-6813},

year  = {2020},

date = {2020-03-01},

urldate = {2024-11-22},

journal = {Business Horizons},

volume = {63},

number = {2},

pages = {215–226},

series = {ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING},

abstract = {AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}